Customer Churn Prediction

Quickly anticipate the churn of your customers, identify churn risk factors, and improve customer relationships using machine learning automation on


Detecting Risk Efficiently

Today, all sectors of activity are concerned with detecting and preventing customer churn. The highly competitive landscape along with the lack the lack of customer loyalty requires stakeholders to know in real-time which customer is likely to churn. With growing customer data, the complexity of variables makes it necessary to have the right analytical tools in place to assist teams.


Reducing Churn with Automated Machine Learning

Generating predictive models is now painless with automated machine learning, which can process a lot of customer data from different sources in real-time. As it crosses data such as customer interactions, purchase history, and new data sources, artificial intelligence enables teams to build highly accurate predictive models for customer churn prediction. It can also be used to recommend the best offer that will most likely retain your valuable customers.

Using for Churn Prediction provides teams with an automated platform to quickly build and deploy machine learning models according to your enterprise data and target. For customer churn prediction, will augment your capabilities so that you can more accurately predict which customer is likely to defect and identify risk factors. Models will also inform your customer retention strategy by helping you determine new opportunities for UpSell and Cross-Sell in your customer base.

Using predictive analytics is a must-have in our business. allowed us to efficiently and rapidly identify customers that were most likely to churn and to deploy our customer support efforts accordingly.

Customer Success Manager, Insurance Company

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